Naive DD

Scoring, M2 Data Science for Social Sciences

Author

Théo Druilhe, Pierre Larose, Nathan Pizzetta, Sigurd Saue

1. Calculate the market value of assets (V)

The market value of assets is estimated as the sum of the market value of equity and total debt

df = read_csv("data.csv")
data <- df %>%
  mutate(
    market_value_equity = market_to_book * book_value, # Market value of equity
    total_assets = book_value,                         # Approximate total assets using book value
    V = market_value_equity + (leverage * book_value)  # Market value of assets = equity + debt
  )

2. Calculate total debt (D)

Total debt is derived using the leverage ratio and book value of assets

data <- data %>%
  mutate(
    D = leverage * book_value # Total debt
  )

3. Calculate the Naive Distance to Default (Naive DD)

Naive DD = 1 - (Debt / Market value of assets)

data <- data %>%
  mutate(
    naive_DD = 1 - (D / V)
  )

4. Select relevant columns to display or save the results

naive_DD_results <- data %>%
  select(fyear, gvkey, market_value_equity, D, V, naive_DD)

5. Merge with Y column and filter

naive_DD_results <- naive_DD_results %>%
  left_join(data %>% select(fyear, gvkey, Y), by = c("fyear", "gvkey"))

naive_DD_filtered <- naive_DD_results %>%
  filter(Y == 1)

Results dataset :

Results dataset (With only Y = 1)

---
title: "Naive DD"
subtitle: "Scoring, M2 Data Science for Social Sciences"
author: "Théo Druilhe, Pierre Larose, Nathan Pizzetta, Sigurd Saue"
format: 
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    theme: 
       light: cerulean
    code-fold: false
    warning: false
    message: false
    toc: true
    toc-depth: 3
    embed-resources: true
    self-contained: true
    code-tools:
      source: true
editor: 
    markdown: 
       wrap: sentence
---

```{r, echo=FALSE}
library(dplyr)
library(readr)
library(DT)
library(RPostgres)
library(lubridate)
```




## 1. Calculate the market value of assets (V)
The market value of assets is estimated as the sum of the market value of equity and total debt

```{r}
df = read_csv("data.csv")
data <- df %>%
  mutate(
    market_value_equity = market_to_book * book_value, # Market value of equity
    total_assets = book_value,                         # Approximate total assets using book value
    V = market_value_equity + (leverage * book_value)  # Market value of assets = equity + debt
  )
```



## 2. Calculate total debt (D)
Total debt is derived using the leverage ratio and book value of assets

```{r}
data <- data %>%
  mutate(
    D = leverage * book_value # Total debt
  )
```


## 3. Calculate the Naive Distance to Default (Naive DD)
Naive DD = 1 - (Debt / Market value of assets)

```{r}
data <- data %>%
  mutate(
    naive_DD = 1 - (D / V)
  )
```


## 4. Select relevant columns to display or save the results

```{r}
naive_DD_results <- data %>%
  select(fyear, gvkey, market_value_equity, D, V, naive_DD)
```



## 5. Merge with Y column and filter
```{r}
naive_DD_results <- naive_DD_results %>%
  left_join(data %>% select(fyear, gvkey, Y), by = c("fyear", "gvkey"))

naive_DD_filtered <- naive_DD_results %>%
  filter(Y == 1)
```

### Results dataset :
```{r, echo = FALSE}
datatable(naive_DD_results, 
          options = list(pageLength = 10,
                         autoWidth = TRUE,
                         searchable = TRUE, 
                         filter = "top"))
```
### Results dataset (With only Y = 1)
```{r, echo = FALSE}
datatable(naive_DD_filtered, 
          options = list(pageLength = 10,
                         autoWidth = TRUE,
                         searchable = TRUE, 
                         filter = "top"))
```